Title:Discrimination of Active and Weakly Active Human BACE1 Inhibitors Using Self-Organizing Map and Support Vector Machine
VOLUME: 19 ISSUE: 6
Author(s):Hang Li, Maolin Wang, Ya-Nan Gong and Aixia Yan
Affiliation:State Key Laboratory of Chemical Resource Engineering, Department of Pharmaceutical Engineering, P.O. Box 53, Beijing University of Chemical Technology, 15 BeiSanHuan East Road, Beijing 100029, P.R. China.
Keywords:Classification models, BACE1 inhibitor, Kohonen’s self-organizing map (SOM), support vector machine (SVM).
Abstract:β-secretase (BACE1) is an aspartyl protease, which is considered as a novel vital target in
Alzheimer's disease therapy. We collected a data set of 294 BACE1 inhibitors, and built six
classification models to discriminate active and weakly active inhibitors using Kohonen’s Self-Organizing Map (SOM) method and Support Vector Machine (SVM) method. Each molecular
descriptor was calculated using the program ADRIANA.Code. We adopted two different methods:
random method and Self-Organizing Map method, for training/test set split. The descriptors were selected by F-score and
stepwise linear regression analysis. The best SVM model Model2C has a good prediction performance on test set with
prediction accuracy, sensitivity (SE), specificity (SP) and Matthews correlation coefficient (MCC) of 89.02%, 90%, 88%,
0.78, respectively. Model 1A is the best SOM model, whose accuracy and MCC of the test set were 94.57% and 0.98,
respectively. The lone pair electronegativity and polarizability related descriptors importantly contributed to bioactivity of
BACE1 inhibitor. The Extended-Connectivity Finger-Prints_4 (ECFP_4) analysis found some vitally key substructural
features, which could be helpful for further drug design research. The SOM and SVM models built in this study can be
obtained from the authors by email or other contacts.
